Action Localization with Class-specific Attention. UNet [1]} is supervised with MIL loss which is not strong enough to predict accurate attention scores. The methods of W-TALC [2]}, Action Graphs [3]}, and Deep Metric [4]} all target action-action separation by employing a co-activity similarity loss. 3C-Net [5]} applied center loss on video-level aggregated features to enhance feature discriminability. Deep Metric [4]} outperforms W-TALC [2]}, Action Graphs [3]}, and 3C-Net [5]} by defining a class-specific metric for each action category.
